Design space exploration for asset development in smart oilfields

Smart oilfields have emerged with recent technological advances incorporating current technical innovations into the decision making process for field asset development. Among the research efforts in smart oilfields, design space exploration (DSE) is one of the most important topics. DSE is extensively used to search for optimal field development strategies. However, efficient realization of DSE poses various challenges, including management of simulation models, application integration, and development of DSE framework.;In this work, we study the design of an integrated environment for DSE in oilfield asset development, focusing on data management and application integration. We propose a data component based methodology, which not only manages the reservoir simulation models used by DSE efficiently, but also greatly improves the storage efficiency. We first identify the underlying structure of the simulation models and decompose them into three types of components: reservoir realization, design and simulator configuration. Our methodology then identifies the duplicate components and guarantees that each component has one physical copy in the data repositories. By separating the logical connections between the models and the components from the physical data files, our methodology provides a clean and efficient way to manage the relationships among the models.;For application integration in DSE, we propose a plug-in based workflow approach. Our approach separates the logic of the DSE worklow from the underlying execution to achieve flexibility and extensibility. We first abstract the DSE process as a series of activities. We feed this high-level abstraction into a workflow execution platform, which executes the activities according to a preset order. In our approach, the activities of the DSE workflow are implemented as plug-ins that can be loaded and unloaded at run-time to achieve the configuration and extension. With the plug-ins and the execution platform, our approach supports the automatic execution, dynamic configuration and extensibility of the DSE workflow. We also design and implement a framework for the DSE workflow. The framework can be configured for, and extended to various design problems. To demonstrate the usefulness of the framework, we apply the framework to a well drilling scheduling design problem. The services in the framework facilitate the configuration required for the design problem. The features provided by the framework also facilitate the performance evaluation of the well drilling schedules by the end users.